{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:PQ7KRIZPE7NDC4U5DPYDY24NHZ","short_pith_number":"pith:PQ7KRIZP","canonical_record":{"source":{"id":"2010.07935","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-10-15T06:42:47Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"51fae076aebb09fc057345d4ea9db404b57bf0b4bb5a85c209e79f5c8ac15a00","abstract_canon_sha256":"45ced4a9b4dc2770ebbdf0ea8e662898742a345a11dfc8d443e26931b33321a4"},"schema_version":"1.0"},"canonical_sha256":"7c3ea8a32f27da31729d1bf03c6b8d3e6f86ab029f72dac75adeb300ff808aaf","source":{"kind":"arxiv","id":"2010.07935","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.07935","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"arxiv_version","alias_value":"2010.07935v1","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.07935","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"pith_short_12","alias_value":"PQ7KRIZPE7ND","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"pith_short_16","alias_value":"PQ7KRIZPE7NDC4U5","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"pith_short_8","alias_value":"PQ7KRIZP","created_at":"2026-07-05T01:43:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:PQ7KRIZPE7NDC4U5DPYDY24NHZ","target":"record","payload":{"canonical_record":{"source":{"id":"2010.07935","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-10-15T06:42:47Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"51fae076aebb09fc057345d4ea9db404b57bf0b4bb5a85c209e79f5c8ac15a00","abstract_canon_sha256":"45ced4a9b4dc2770ebbdf0ea8e662898742a345a11dfc8d443e26931b33321a4"},"schema_version":"1.0"},"canonical_sha256":"7c3ea8a32f27da31729d1bf03c6b8d3e6f86ab029f72dac75adeb300ff808aaf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:43:29.254561Z","signature_b64":"mFE1va8CJEXEZAFY+uPaaudi8XdSPpnlxtEwH2tnah8yXAtVLvVQ2wpHTXNKpPbz6swpLSss97EPZGDqYFCmDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7c3ea8a32f27da31729d1bf03c6b8d3e6f86ab029f72dac75adeb300ff808aaf","last_reissued_at":"2026-07-05T01:43:29.254108Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:43:29.254108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.07935","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:43:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6tba/U8Mtk5k3MsC1rX/9sYdHYoIZqfvqDb8rvqb1V2fYtIV6DmnlEBilWsOY4arFqhbIlmVl7+BX76Ou4kSBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T16:23:51.293566Z"},"content_sha256":"bed8b540671a8090ee4567121b287abcf0e89c4d68cd33a15fe1ee804201fd73","schema_version":"1.0","event_id":"sha256:bed8b540671a8090ee4567121b287abcf0e89c4d68cd33a15fe1ee804201fd73"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:PQ7KRIZPE7NDC4U5DPYDY24NHZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Agent Motion Planning using Deep Learning for Space Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Amir Rahmani, Anthony Davis, Changrak Choi, Kyongsik Yun, Linda Forster, Muhammad Adil, Ramtin Madani, Ryan Alimo","submitted_at":"2020-10-15T06:42:47Z","abstract_excerpt":"State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.07935","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.07935/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:43:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"96H38VXQ2IgayXbue2pTv2z9GiMEmv+qZe5PuMoB3a3Yk9KYTOVnBw0zKATjrJ+w1BYaDKCTy6EsJqWfMB6OCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T16:23:51.293948Z"},"content_sha256":"0d2217ebb629f87201a7463b4493a5d60358e25dae17b87fc5d4fecd83b01394","schema_version":"1.0","event_id":"sha256:0d2217ebb629f87201a7463b4493a5d60358e25dae17b87fc5d4fecd83b01394"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PQ7KRIZPE7NDC4U5DPYDY24NHZ/bundle.json","state_url":"https://pith.science/pith/PQ7KRIZPE7NDC4U5DPYDY24NHZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PQ7KRIZPE7NDC4U5DPYDY24NHZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T16:23:51Z","links":{"resolver":"https://pith.science/pith/PQ7KRIZPE7NDC4U5DPYDY24NHZ","bundle":"https://pith.science/pith/PQ7KRIZPE7NDC4U5DPYDY24NHZ/bundle.json","state":"https://pith.science/pith/PQ7KRIZPE7NDC4U5DPYDY24NHZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PQ7KRIZPE7NDC4U5DPYDY24NHZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:PQ7KRIZPE7NDC4U5DPYDY24NHZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"45ced4a9b4dc2770ebbdf0ea8e662898742a345a11dfc8d443e26931b33321a4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-10-15T06:42:47Z","title_canon_sha256":"51fae076aebb09fc057345d4ea9db404b57bf0b4bb5a85c209e79f5c8ac15a00"},"schema_version":"1.0","source":{"id":"2010.07935","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.07935","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"arxiv_version","alias_value":"2010.07935v1","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.07935","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"pith_short_12","alias_value":"PQ7KRIZPE7ND","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"pith_short_16","alias_value":"PQ7KRIZPE7NDC4U5","created_at":"2026-07-05T01:43:29Z"},{"alias_kind":"pith_short_8","alias_value":"PQ7KRIZP","created_at":"2026-07-05T01:43:29Z"}],"graph_snapshots":[{"event_id":"sha256:0d2217ebb629f87201a7463b4493a5d60358e25dae17b87fc5d4fecd83b01394","target":"graph","created_at":"2026-07-05T01:43:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2010.07935/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be","authors_text":"Amir Rahmani, Anthony Davis, Changrak Choi, Kyongsik Yun, Linda Forster, Muhammad Adil, Ramtin Madani, Ryan Alimo","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-10-15T06:42:47Z","title":"Multi-Agent Motion Planning using Deep Learning for Space Applications"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.07935","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:bed8b540671a8090ee4567121b287abcf0e89c4d68cd33a15fe1ee804201fd73","target":"record","created_at":"2026-07-05T01:43:29Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"45ced4a9b4dc2770ebbdf0ea8e662898742a345a11dfc8d443e26931b33321a4","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2020-10-15T06:42:47Z","title_canon_sha256":"51fae076aebb09fc057345d4ea9db404b57bf0b4bb5a85c209e79f5c8ac15a00"},"schema_version":"1.0","source":{"id":"2010.07935","kind":"arxiv","version":1}},"canonical_sha256":"7c3ea8a32f27da31729d1bf03c6b8d3e6f86ab029f72dac75adeb300ff808aaf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7c3ea8a32f27da31729d1bf03c6b8d3e6f86ab029f72dac75adeb300ff808aaf","first_computed_at":"2026-07-05T01:43:29.254108Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:43:29.254108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mFE1va8CJEXEZAFY+uPaaudi8XdSPpnlxtEwH2tnah8yXAtVLvVQ2wpHTXNKpPbz6swpLSss97EPZGDqYFCmDw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:43:29.254561Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.07935","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bed8b540671a8090ee4567121b287abcf0e89c4d68cd33a15fe1ee804201fd73","sha256:0d2217ebb629f87201a7463b4493a5d60358e25dae17b87fc5d4fecd83b01394"],"state_sha256":"da96ba31ea0f3c994a84ffac752aaeac4ffe009f59894e35f940310c6424fe54"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mcD+Khld+AfYhpdcZoTc0SdwMdkL6Xong9o/At6GRfRrom7p9DDidFlSVZG/tg9OzMSTI0e7UMUve9dwwSTZBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T16:23:51.295990Z","bundle_sha256":"eb8a90b8d2a9bc144f1736845459f9d132abf6d4d57e22aeba60de250de9c387"}}